Goal Programming for AI-Driven Sustainable City Planning

Abstract

The rapid urbanization has compounded the issues of traffic jams, increased CO 2 emissions, increased energy requirements, and increased infrastructure expenditure. Conventional methods of urban planning currently tend to address these problems separately leading to the lack of coordination of policies and the waste of resources. This paper postulates a hybrid Artificial Intelligence-Goal Programming (AI-GP) platform of sustainable urban planning integrating predictive power of machine learning with the multi-objective optimization power of Goal programming. Within the framework suggested, the AI/ML models will be utilized to predict such important urban-level indicators as traffic congestion, CO 2 emissions and energy demand with the use of historic city data. These forecasts are then added as targets to a Goal Programming model which will maximize a multitude of sustainability goals including traffic congestion, emissions, the use of the public transport and the cost of the infrastructure investment. An example of a numerical case study illustrates the relevance of the model and emphasizes the effectiveness of the framework to balance the environmental, mobility, and economic goals and identifies trade-offs between competing goals. The validation has shown that the AI- GP methodology offers more policy-oriented and balanced solutions than AI- only or single-objective models of optimization. The research has added a new hybrid system of decision support that will provide connections between data-driven urban intelligence and optimization of the policy that will allow transparent, evidence-based, and sustainable urban development. The suggested solution can assist the policymakers and urban planners to create more intelligent and resilient cities consistent with the long-term sustainability objectives.